CN117591714A - Service data matching method and device, electronic equipment and storage medium - Google Patents

Service data matching method and device, electronic equipment and storage medium Download PDF

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Publication number
CN117591714A
CN117591714A CN202311617605.0A CN202311617605A CN117591714A CN 117591714 A CN117591714 A CN 117591714A CN 202311617605 A CN202311617605 A CN 202311617605A CN 117591714 A CN117591714 A CN 117591714A
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data
service
service data
matching
matched
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梁辰
张嵬
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China Post Information Technology Beijing Co ltd
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China Post Information Technology Beijing Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/903—Querying
    • G06F16/9035—Filtering based on additional data, e.g. user or group profiles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Abstract

The invention discloses a service data matching method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring first service data and second service data in a target service database; grouping the first service data and the second service data according to the service association information to obtain at least one data group to be matched; wherein the data group to be matched comprises a first service data and at least one second service data; performing money amount matching on the first service data and the second service data in the current data set to be matched aiming at each data set to be matched to obtain a matching result; and if the matching result is successful, outputting a money amount matching scheme corresponding to the current data set to be matched. The problem that the verification speed is low when the data verification is carried out on the service data based on the exhaustion method in the prior art is solved, and the effect of improving the automatic verification speed on the premise of ensuring the accuracy of the service verification is achieved.

Description

Service data matching method and device, electronic equipment and storage medium
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a service data matching method, a device, an electronic device, and a storage medium.
Background
With the explosion of express service, the automatic verification and sales amount associated with each express point is greatly increased.
In practical application, the total money amount of the running water and the bill which are required to be matched by automatic verification is equal, and the automatic verification is generally divided into three scenes, namely: one running water corresponds to a plurality of documents, one running water corresponds to one document, and a plurality of running water corresponds to one document. Currently, when automatic verification is performed, an exhaustion method is mostly used for calculating potential matching relation between running water and a bill. However, the exhaustion method has the problem of low matching efficiency due to the large increase of the automatic sales quantity.
In order to solve the above problems, an improvement is required for the way of matching traffic data in automatic verification.
Disclosure of Invention
The invention provides a service data matching method, a device, electronic equipment and a storage medium, which are used for solving the problem that the verification speed is low when service data is verified based on an exhaustion method in the prior art.
In a first aspect, an embodiment of the present invention provides a service data matching method, including:
acquiring first service data and second service data in a target service database; when the first service data is service stream data, the second service data is service bill data; if the first service data is service bill data, the second service data is service stream data;
Grouping the first service data and the second service data according to the service association information to obtain at least one data group to be matched; wherein the data group to be matched comprises a first service data and at least one second service data;
performing money amount matching on the first service data and the second service data in the current data set to be matched aiming at each data set to be matched to obtain a matching result;
and if the matching result is that the matching is successful, outputting a money amount matching scheme corresponding to the current data set to be matched.
In a second aspect, an embodiment of the present invention further provides a service data matching device, including:
the data acquisition module is used for acquiring first service data and second service data in the target service database; when the first service data is service stream data, the second service data is service bill data; if the first service data is service bill data, the second service data is service stream data;
the data set determining module is used for grouping the first service data and the second service data according to the service association information to obtain at least one data set to be matched; wherein the data group to be matched comprises a first service data and at least one second service data;
The matching result determining module is used for carrying out money amount matching on the first service data and the second service data in the current data set to be matched aiming at each data set to be matched to obtain a matching result;
and the matching scheme determining module is used for outputting a money amount matching scheme corresponding to the current data set to be matched if the matching result is that the matching is successful.
In a third aspect, an embodiment of the present invention further provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the traffic data matching method according to any one of the embodiments of the present invention.
In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium, where the computer readable storage medium stores computer instructions, where the computer instructions are configured to cause a processor to implement the service data matching method according to any one of the embodiments of the present invention when executed.
According to the technical scheme, through acquiring first service data and second service data in a target service database, grouping the first service data and the second service data according to service association information to obtain at least one data group to be matched, and performing money amount matching on the first service data and the second service data in the current data group to be matched according to each data group to be matched to obtain a matching result; and if the matching result is successful, outputting a money amount matching scheme corresponding to the current data set to be matched. In the technical scheme, the first service data and the second service data associated with the same user are divided into a data group to be matched by grouping the service data in the target service database, so that the first service data and the second service data corresponding to the same user are quickly and accurately verified, and abnormal service data can be conveniently checked when verification is abnormal. Further, the technical scheme adopts a knapsack algorithm to carry out knapsack-article matching on the first service data and the second service data in the data group to be matched, so as to obtain an optimal matching scheme of the first service data and the second service data, and when the first service data and the second service data are completely matched, a corresponding money amount matching scheme is output. The problem that the verification speed is low when the data verification is carried out on the service data based on the exhaustion method in the prior art is solved, and the effect of improving the automatic verification speed on the premise of ensuring the accuracy of the service verification is achieved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a service data matching method according to a first embodiment of the present invention;
fig. 2 is a flowchart of a service data matching method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a service data matching device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing a service data matching method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein.
Example 1
Fig. 1 is a flowchart of a service data matching method provided in an embodiment of the present invention, where the embodiment may be adapted to group service flow data and service receipt data in a service system, use service flow data and service receipt data corresponding to the same user as a data group to be matched, and perform automatic verification on service data in each data group to be matched, where the method may be performed by a service data matching device, where the service data matching device may be implemented in hardware and/or software, and where the service data matching device may be configured in a computing device that may perform the service data matching method.
As shown in fig. 1, the method includes:
s110, acquiring first service data and second service data in a target service database.
The target business database is a database which contains business flow data and business bill data and needs to be automatically verified. For example, the target business database may be a business database of a certain financial institution, a business database of a certain express point, or the like.
In practical application, the automatic verification includes a case that one piece of flow data corresponds to one piece of bill data, one piece of flow data corresponds to a plurality of pieces of bill data, and a plurality of pieces of flow water corresponds to one bill, and the technical scheme is mainly used for improving the application scene that one piece of flow data corresponds to a plurality of pieces of bill data or a plurality of pieces of flow data corresponds to a plurality of pieces of bill data for automatic verification.
It can be understood that, in a scenario where one piece of stream data corresponds to a plurality of bill data or a scenario where a plurality of bill data corresponds to one piece of stream data, the essence is a one-to-many data matching scenario. Based on the above, the technical scheme uses one scene to automatically cancel and illustrate, optionally, if the first service data is service flow data, the second service data is service bill data; and if the first service data is the service bill data, the second service data is the service stream data.
Optionally, acquiring the first service data and the second service data in the target service database includes: the method comprises the steps of obtaining a first service data table and a second service data table from a target service database, obtaining at least one piece of first service data according to the first service data table, and obtaining at least one piece of second service data based on the second service data table.
The first service data table refers to a data table used for recording first service data in the target service database, and the second service data table refers to a data table used for recording second service data in the target service database.
In practical applications, in order to facilitate statistics of service data generated during service occurrence, a user typically performs corresponding recording during service occurrence. Taking a certain financial institution as an example, the financial institution generates corresponding business flow data when carrying out transactions, and provides corresponding business bill data for users of the transactions. When automatic verification processing is needed, the financial institution performs verification data matching with service flow data and service bill data corresponding to the same user to obtain a matching result, and whether the service verification between the financial institution and the user is finished is determined according to the matching result. Specifically, if the business flow data and the business bill data are exactly matched, the verification is finished, otherwise, the verification of the business between the financial institution and the user is not finished.
S120, grouping the first service data and the second service data according to the service association information to obtain at least one data group to be matched.
The service related information refers to user identification, service handling date and other information carried in the service data. The data set to be matched comprises a first service data and at least one second service data.
In the technical scheme, grouping the first service data and the second service data according to the service association information to obtain at least one data group to be matched comprises the following steps: acquiring first service association information corresponding to current first service data aiming at least one piece of first service data; according to second service association information carried in the second service data, the second service data matched with the first service association information is used as associated service data corresponding to the current first service data; and obtaining a data group to be matched corresponding to the current first service data based on at least one piece of associated service data.
The first service association information comprises a user identifier carried by the first service data and a service handling date. The second service association information comprises a user identifier carried by second service data and a service handling date;
In practical application, in order to improve the matching speed and matching accuracy of data of service stream data and service receipts, in the technical scheme, at least one piece of first service data is combined according to the first service association information, and the first service data associated with the same user is divided into the same group. Further, for the first service data group corresponding to any user, determining service data corresponding to each user from at least one piece of second service data according to the second service association information, and taking the first service data and the second service data associated with the same user as a data group to be matched.
For example, taking the first service data as service stream data and the second service data as service bill data as an example. 100 business flow data are recorded in a first business data table of a target business database, the 100 business flow data are assigned to 5 users, 110 business bill data are recorded in a second business data table, and the 110 business bill data are assigned to 6 users. In the technical scheme, firstly, 100 pieces of service stream data can be divided into stream service groups corresponding to 5 users according to user information and service handling dates in the service stream data. Taking the aggregate service group corresponding to the user 1 as an example, the number of service aggregate data associated with the user 1 is 10, in order to determine whether the transaction between the user 1 and the service organization is finished or not, mainly according to whether the service aggregate data and the service bill data between the user 1 and the service organization can be verified or not, if so, the transaction between the user 1 and the service organization is finished. Based on the above, according to the second service association information, the service bill data associated with the user 1 is determined from at least one piece of service bill data, for example, the service bill data associated with the user 1 is 12 pieces, and after data matching is performed based on the service association information, the obtained data set to be matched corresponding to the user 1 includes 10 pieces of service stream data and 12 pieces of service bill data.
S130, carrying out money amount matching on the first service data and the second service data in the current data set to be matched according to each data set to be matched, and obtaining a matching result.
In the technical scheme, at least one data group to be matched can be obtained by grouping the first service data and the second service data in the target service database.
Further, taking one of the data sets to be matched as an example, the technical scheme can adopt a knapsack algorithm to match the amount of money with the first service data and the second service data in the data sets to be matched. Specifically, the first business data in the data set to be matched can be used as a knapsack, and the second business data in the data set to be matched can be used as an article to be loaded into the knapsack. Based on this, the first traffic data becomes the "knapsack capacity"; each second business datum becomes "quality of the item".
Specifically, taking first service data as service flow data and second service data as service bill data as examples, respectively initializing a 'knapsack capacity' list and a 'quality of articles' list in a 'knapsack algorithm' of the service flow data and the service bill data, and matching the service flow data and the service bill data in a data group to be matched based on the knapsack algorithm to obtain a matching result.
Backpack capacity = { T 1 ,T 2 ，T 3 ，……,T m }
Article mass = { S 1 ,S 2 ，S 3 ，……,S n }
Wherein T is m Representing business flow data S n Representing business document data.
In the technical scheme, m is E N + ，n∈N + 。
And S140, if the matching result is that the matching is successful, outputting a money amount matching scheme corresponding to the current data set to be matched.
If the matching result is successful, the matching result indicates that the service flow data and the service bill data in the current data set to be matched are completely matched, and then the corresponding money amount matching scheme can be adopted.
According to the technical scheme, through acquiring first service data and second service data in a target service database, grouping the first service data and the second service data according to service association information to obtain at least one data group to be matched, and performing money amount matching on the first service data and the second service data in the current data group to be matched according to each data group to be matched to obtain a matching result; and if the matching result is successful, outputting a money amount matching scheme corresponding to the current data set to be matched. In the technical scheme, the first service data and the second service data associated with the same user are divided into a data group to be matched by grouping the service data in the target service database, so that the first service data and the second service data corresponding to the same user are quickly and accurately verified, and abnormal service data can be conveniently checked when verification is abnormal. Further, the technical scheme adopts a knapsack algorithm to carry out knapsack-article matching on the first service data and the second service data in the data group to be matched, so as to obtain an optimal matching scheme of the first service data and the second service data, and when the first service data and the second service data are completely matched, a corresponding money amount matching scheme is output. The problem that the verification speed is low when the data verification is carried out on the service data based on the exhaustion method in the prior art is solved, and the effect of improving the automatic verification speed on the premise of ensuring the accuracy of the service verification is achieved.
Example two
Fig. 2 is a flowchart of a service data matching method according to a second embodiment of the present invention, and optionally, performing money amount matching on the first service data and the second service data in the current data set to be matched, so as to refine the matching result.
As shown in fig. 2, the method includes:
s210, acquiring first service data and second service data in a target service database.
S220, grouping the first service data and the second service data according to the service association information to obtain at least one data group to be matched.
S230, determining a Boolean variable matrix corresponding to the current data set to be matched according to the first service data and the second service data in the current data set to be matched aiming at each data set to be matched.
In a specific example, business flow data in a data set to be matched is taken as a knapsack individual, business bill data is taken as an article individual, and a Boolean variable matrix is created based on an initialized matching parameter, wherein the Boolean variable matrix is as follows:
wherein m represents the number of backpack individuals, n represents the number of article individuals, and X n_m Representing a boolean variable.
Wherein X is n_m The value of (2) is 0 or 1, which is used to describe whether the nth article is to be placed in the mth backpack, and when the value is 0, the article is not placed in the mth backpack, and when the value is 1, the article is placed in the mth backpack.
S240, performing matrix constraint on the Boolean variable matrix based on at least one constraint condition to obtain a target matching model.
In this technical solution, the at least one constraint condition includes a first constraint condition corresponding to the first service data and a second constraint condition corresponding to the second service data, and the matrix constraint is performed on the boolean variable matrix based on the at least one constraint condition to obtain a target matching model, including: taking each piece of sub-data in the first service data as an article individual, and taking each piece of sub-data in the second service data as a knapsack individual; carrying out item quantity matching constraint on the item individuals based on the first constraint condition to obtain a first constraint model; performing capacity constraint on the backpack individual based on the second constraint condition to obtain a second constraint model; and constructing a target matching model corresponding to the current data set to be matched based on the first constraint model and the second constraint model.
Wherein each item individual corresponds to zero backpack individuals or to one backpack individual. Each individual backpack may be used to carry zero or one individual item.
In practice, since each "item individual" can only be put into one "backpack individual", the variable matrix defined above is boolean, so a first constraint model can be created based on each "item individual":
Wherein m represents the number of backpack individuals, n represents the number of article individuals, and X n_m Representing a boolean variable.
It will be appreciated that each "individual item" may create an inequality in the constraint model described above, with the value ranges [0,1] on either side of the inequality, meaning that each "individual item" can only be placed 0 times (not placed) or 1 time (placed) in a "individual backpack". The expression in the middle of the inequality is used to describe which "backpack individual" the item individual is put into, and the coefficients in front of the variables are unified to be 1.
Further, since the capacity of each "backpack individual" is limited, the combination of putting "item individuals" is arbitrary on the premise of capacity license, and therefore, a second constraint model can be created based on each "backpack individual":
wherein T is m Representing business flow data S n Represents business bill data, m represents knapsack number, n represents article number, X n_m Representing a boolean variable.
Each "backpack individual" may create an inequality in the constraint model described above. The value ranges [0, tm ] on either side of the inequality, i.e., the range that each "backpack individual" can fit into the "item individual" mass (the "backpack individual" can be empty, but cannot exceed the backpack individual's capacity). The expression in the middle of the inequality is used to describe which "item individuals" the "backpack individual" has placed in, the coefficients preceding the variables being the quality of the corresponding "item individuals".
Because the final goal of matching is to match the business flow data and the business bill data as completely as possible on the basis of meeting business conditions, that is to say, to put all the 'article individuals' into the 'knapsack individuals' as far as possible, based on the matching, a target matching model can be constructed according to the first constraint model and the second constraint model, and the target matching model is as follows:
max{S 1 * X 1_1 +S 1 * X 1_2 +…+S 1 * X 1_m +S 2 * X 2_1 +S 2 * X 2_2 +…+S 2 * X 2_m +…+S n * X n_1 +S n * X n_2 +…+S n * X n_m }
wherein S is n Representing business bill data, m representing the number of knapsack individuals, n representing the number of article individuals, X n_m Representing a boolean variable.
S250, performing money amount matching on the first service data and the second service data in the current data set to be matched based on the target matching model to obtain a matching result.
Further, based on the target matching model, performing money amount matching on the first service data and the second service data in the current data set to be matched to obtain a matching result, including: introducing a relaxation variable into the target matching model to obtain an equation set to be solved; and (3) according to the approximate solution corresponding to the equation set to be solved, obtaining the matching of the amount of money with the current data set to be matched, and obtaining a matching result.
In a specific example, a relaxation variable is introduced into the target matching model to obtain the following equation set to be solved:
Wherein the relaxation variable R 1_1_1 ∈R，R 1_1_2 ∈R，R 2_m_1 ∈R，R 2_m_2 ∈R，m∈N + ，n∈N + 。
The equation set is solved through a linear programming algorithm, a plurality of feasible poles can be obtained, and the optimal solution can be further solved by combining the target matching model in the technical scheme. Further, an accurate solution is obtained according to the approximate solution: if the sum of the quality of each 'article individual' is exactly equal to the capacity of the 'knapsack individual', the matching mode is correct and feasible, namely, the matching result is that the matching is successful, the matching information is stored in a return list, and the money amount matching scheme is output. If the sum of the quality of each item is smaller than the capacity of each knapsack, the matching mode is not in accordance with the requirement, namely the matching result is unsuccessful.
And S260, if the matching result is unsuccessful, the first service data and the second service data in the current data group to be matched are sent to a manual auditing system for auditing.
According to the technical scheme, through acquiring first service data and second service data in a target service database, grouping the first service data and the second service data according to service association information to obtain at least one data group to be matched, and performing money amount matching on the first service data and the second service data in the current data group to be matched according to each data group to be matched to obtain a matching result; and if the matching result is successful, outputting a money amount matching scheme corresponding to the current data set to be matched. In the technical scheme, the first service data and the second service data associated with the same user are divided into a data group to be matched by grouping the service data in the target service database, so that the first service data and the second service data corresponding to the same user are quickly and accurately verified, and abnormal service data can be conveniently checked when verification is abnormal. Further, the technical scheme adopts a knapsack algorithm to carry out knapsack-article matching on the first service data and the second service data in the data group to be matched, so as to obtain an optimal matching scheme of the first service data and the second service data, when the first service data and the second service data are completely matched, a corresponding money amount matching scheme is output, and if the first service data and the second service data cannot be completely matched, the first service data and the second service data in the corresponding data group to be matched are sent to a manual checking system for manual checking, so that the accuracy of service data verification is ensured. The problem that the verification speed is low when the data verification is carried out on the service data based on the exhaustion method in the prior art is solved, and the effect of improving the automatic verification speed on the premise of ensuring the accuracy of the service verification is achieved.
Example III
Fig. 3 is a schematic structural diagram of a service data matching device according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes: a data acquisition module 310, a data set determination module 320, a matching result determination module 330, and a matching scheme determination module 340.
The data acquisition module 310 is configured to acquire first service data and second service data in the target service database; when the first service data is service stream data, the second service data is service bill data; if the first service data is the service bill data, the second service data is the service stream data;
a data set determining module 320, configured to group the first service data and the second service data according to the service association information, so as to obtain at least one data set to be matched; wherein the data group to be matched comprises a first service data and at least one second service data;
the matching result determining module 330 is configured to perform, for each data set to be matched, a payment amount matching on the first service data and the second service data in the current data set to be matched, to obtain a matching result;
and the matching scheme determining module 340 is configured to output a money amount matching scheme corresponding to the current data set to be matched if the matching result is that the matching is successful.
According to the technical scheme, through acquiring first service data and second service data in a target service database, grouping the first service data and the second service data according to service association information to obtain at least one data group to be matched, and performing money amount matching on the first service data and the second service data in the current data group to be matched according to each data group to be matched to obtain a matching result; and if the matching result is successful, outputting a money amount matching scheme corresponding to the current data set to be matched. In the technical scheme, the first service data and the second service data associated with the same user are divided into a data group to be matched by grouping the service data in the target service database, so that the first service data and the second service data corresponding to the same user are quickly and accurately verified, and abnormal service data can be conveniently checked when verification is abnormal. Further, the technical scheme adopts a knapsack algorithm to carry out knapsack-article matching on the first service data and the second service data in the data group to be matched, so as to obtain an optimal matching scheme of the first service data and the second service data, and when the first service data and the second service data are completely matched, a corresponding money amount matching scheme is output. The problem that the verification speed is low when the data verification is carried out on the service data based on the exhaustion method in the prior art is solved, and the effect of improving the automatic verification speed on the premise of ensuring the accuracy of the service verification is achieved.
Optionally, the data acquisition module is configured to acquire a first service data table and a second service data table from the target service database, obtain at least one piece of first service data according to the first service data table, and obtain at least one piece of second service data based on the second service data table.
Optionally, the data set determining module includes: the association information determining sub-module is used for acquiring first service association information corresponding to current first service data aiming at least one piece of first service data; the first business association information comprises a user identifier carried by first business data and business handling date;
the associated service data determining sub-module is used for taking the second service data matched with the first service associated information as associated service data corresponding to the current first service data according to the second service associated information carried in the second service data; the second service association information comprises a user identifier carried by second service data and a service handling date;
and the data set determining sub-module is used for obtaining a data set to be matched corresponding to the current first service data based on at least one piece of associated service data.
Optionally, the matching result determining module includes: the matrix determining submodule is used for determining a Boolean variable matrix corresponding to the current data set to be matched according to the first service data and the second service data in the current data set to be matched;
The target matching model determining submodule is used for carrying out matrix constraint on the Boolean variable matrix based on at least one constraint condition to obtain a target matching model;
and the matching result determining sub-module is used for carrying out money amount matching on the first service data and the second service data in the current data set to be matched based on the target matching model to obtain a matching result. .
Optionally, the object matching model determining submodule includes: the individual definition unit is used for taking each piece of sub-data in the first service data as an article individual and taking each piece of sub-data in the second service data as a knapsack individual;
the first constraint model determining unit is used for carrying out item quantity matching constraint on the item individuals based on the first constraint condition to obtain a first constraint model; wherein each item individual corresponds to zero backpack individuals or to one backpack individual;
the second constraint model determining unit is used for carrying out capacity constraint on the backpack individual based on the second constraint condition to obtain a second constraint model; wherein each backpack individual may be used to carry zero or one item individual;
the target matching model determining unit is used for constructing a target matching model corresponding to the current data set to be matched based on the first constraint model and the second constraint model.
Optionally, the matching result determining submodule includes: the equation set determining unit is used for introducing a relaxation variable into the target matching model to obtain an equation set to be solved;
and the matching result determining unit is used for obtaining the amount of money matched with the current data set to be matched according to the approximate solution corresponding to the equation set to be solved, and obtaining a matching result.
Optionally, the service data matching device is further configured to send the first service data and the second service data in the current data set to be matched to the manual auditing system for auditing if the matching result is unsuccessful.
The service data matching device provided by the embodiment of the invention can execute the service data matching method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 4 shows a schematic structural diagram of the electronic device 10 of the embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the traffic data matching method.
In some embodiments, the business data matching method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the business data matching method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the traffic data matching method in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
The computer program for implementing the business data matching method of the present invention can be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims (10)

1. A method for matching service data, comprising:
acquiring first service data and second service data in a target service database; when the first service data is service stream data, the second service data is service bill data; if the first service data is service bill data, the second service data is service stream data;
grouping the first service data and the second service data according to the service association information to obtain at least one data group to be matched; wherein the data group to be matched comprises a first service data and at least one second service data;
Performing money amount matching on the first service data and the second service data in the current data set to be matched aiming at each data set to be matched to obtain a matching result;
and if the matching result is that the matching is successful, outputting a money amount matching scheme corresponding to the current data set to be matched.
2. The method of claim 1, wherein the obtaining the first service data and the second service data in the target service database comprises:
and acquiring a first service data table and a second service data table from the target service database, acquiring at least one piece of first service data according to the first service data table, and acquiring at least one piece of second service data based on the second service data table.
3. The method of claim 1, wherein grouping the first service data and the second service data according to service association information to obtain at least one data group to be matched comprises:
acquiring first service association information corresponding to current first service data aiming at least one piece of first service data; the first service association information comprises a user identifier carried by the first service data and a service handling date;
According to second service association information carried in second service data, taking the second service data matched with the first service association information as associated service data corresponding to the current first service data; the second service association information comprises a user identifier carried by the second service data and a service handling date;
and obtaining a data group to be matched corresponding to the current first service data based on at least one piece of associated service data.
4. The method according to claim 1, wherein the performing the money amount matching on the first service data and the second service data in the current data set to be matched to obtain a matching result includes:
determining a Boolean variable matrix corresponding to the current data set to be matched according to the first service data and the second service data in the current data set to be matched;
performing matrix constraint on the Boolean variable matrix based on at least one constraint condition to obtain a target matching model;
and carrying out money amount matching on the first service data and the second service data in the current data set to be matched based on the target matching model to obtain a matching result.
5. The method of claim 4, wherein the at least one constraint comprises a first constraint corresponding to the first business data and a second constraint corresponding to the second business data, wherein the performing matrix constraint on the boolean variable matrix based on the at least one constraint to obtain a target matching model comprises:
taking each piece of sub-data in the first service data as an article individual, and taking each piece of sub-data in the second service data as a knapsack individual;
performing item quantity matching constraint on the item individuals based on the first constraint condition to obtain a first constraint model; wherein each item individual corresponds to zero backpack individuals or to one backpack individual;
performing capacity constraint on the backpack individual based on the second constraint condition to obtain a second constraint model; wherein each backpack individual may be used to carry zero or one item individual;
and constructing a target matching model corresponding to the current data set to be matched based on the first constraint model and the second constraint model.
6. The method according to claim 4, wherein the performing, based on the target matching model, the money amount matching on the first service data and the second service data in the current data set to be matched to obtain a matching result includes:
Introducing a relaxation variable into the target matching model to obtain an equation set to be solved;
and according to the approximate solution corresponding to the equation set to be solved, obtaining the matching of the amount of money with the current data set to be matched, and obtaining a matching result.
7. The method as recited in claim 1, further comprising:
and if the matching result is unsuccessful, sending the first service data and the second service data in the current data group to be matched to a manual auditing system for auditing.
8. A traffic data matching apparatus, comprising:
the data acquisition module is used for acquiring first service data and second service data in the target service database; when the first service data is service stream data, the second service data is service bill data; if the first service data is service bill data, the second service data is service stream data;
the data set determining module is used for grouping the first service data and the second service data according to the service association information to obtain at least one data set to be matched; wherein the data group to be matched comprises a first service data and at least one second service data;
The matching result determining module is used for carrying out money amount matching on the first service data and the second service data in the current data set to be matched aiming at each data set to be matched to obtain a matching result;
and the matching scheme determining module is used for outputting a money amount matching scheme corresponding to the current data set to be matched if the matching result is that the matching is successful.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the traffic data matching method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the traffic data matching method of any one of claims 1-7.
CN202311617605.0A 2023-11-29 2023-11-29 Service data matching method and device, electronic equipment and storage medium Pending CN117591714A (en)

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